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Novel use of diamond in eye-safe lasers
Get PDFThis thesis was previously held under moratorium from 9th October 2020 to 9th October 2022.The phenomenon of stimulated Raman scattering is one of the ways of accessing a laser emission spectrum. This thesis focuses on converting a 1µm laser to the so called eye-safe 1.5µm wavelength useful in e.g. LIDAR applications. The novelty of this approach is that compared to ion based lasers e.g. Er:fiber, Raman lasers are mostly limited by the pump power, not by the thermal roll off, especially when using materials such as diamond with extremely high thermal conductivity. This is subjected to the absorption in the Raman crystal and other effects described more in detail in the introduction. The work presents firstly a design of a proof of concept, low power and repetition rate pump source at 1µm based on Nd:YAG and a an external cavity Raman diamond laser. This laser was investigated to find the optimum operating conditions such as pump intensity, output coupling transmission and a laser cavity mode size. These findings were then applied in high average power Diamond Raman laser. These experiments used an Yb based fiber master oscillator, power amplifier which acted as a pump source. These experiments concluded with obtaining over 10W of average power at 150kHz of laser emission above 1.5µm wavelength. This method of generating eye-safe wavelengths is a promising way to high power and repetition rate emission. A novel approach of generating high average power especially with a narrow linewidth emission is also presented by using a Raman cavity in an amplifier configuration. A first diamond based, second Stokes Raman amplifier operating at eye-safe wavelengths regime is presented in Chapter 5.The phenomenon of stimulated Raman scattering is one of the ways of accessing a laser emission spectrum. This thesis focuses on converting a 1µm laser to the so called eye-safe 1.5µm wavelength useful in e.g. LIDAR applications. The novelty of this approach is that compared to ion based lasers e.g. Er:fiber, Raman lasers are mostly limited by the pump power, not by the thermal roll off, especially when using materials such as diamond with extremely high thermal conductivity. This is subjected to the absorption in the Raman crystal and other effects described more in detail in the introduction. The work presents firstly a design of a proof of concept, low power and repetition rate pump source at 1µm based on Nd:YAG and a an external cavity Raman diamond laser. This laser was investigated to find the optimum operating conditions such as pump intensity, output coupling transmission and a laser cavity mode size. These findings were then applied in high average power Diamond Raman laser. These experiments used an Yb based fiber master oscillator, power amplifier which acted as a pump source. These experiments concluded with obtaining over 10W of average power at 150kHz of laser emission above 1.5µm wavelength. This method of generating eye-safe wavelengths is a promising way to high power and repetition rate emission. A novel approach of generating high average power especially with a narrow linewidth emission is also presented by using a Raman cavity in an amplifier configuration. A first diamond based, second Stokes Raman amplifier operating at eye-safe wavelengths regime is presented in Chapter 5
Solid-state control and analysis of active pharmaceutical ingredients
No full textThe research described herein entails solid-state screening investigations and characterisation of a number of active pharmaceutical ingredients to improve understanding of the range of structural diversity possible and specific factors that impact on specific systems. The pharmaceuticals investigated are the anticonvulsant oxcarbazepine (OXCBZ), the developmental cholesterylester transfer protein inhibitor evacetrapib (EVC) and a novel tetrazole analogue of EVC (TAEVC) that has been custom synthesised by Eli Lilly and Company. All three compounds were subjected to both solvent-based and solvent-free crystallisation protocols to target factors promoting the formation of novel crystal structures, characterise any novel crystal packing arrangements formed and explore how non-classical nucleation and growth mechanisms impact crystal morphology.;Experimental screening studies of OXCBZ enabled form III to be obtained via solution crystallisation and from physical vapour deposition onto various substrates. The R-3 crystal structure of this polymorph was determined for the first time using X-ray powder diffraction. OXCBZ form III was also found to crystallise with a striking twisted morphology. Detailed analysis using scanning electron and atomic force microscopy allowed tracking of the formation and evolution of the twisted crystals. Twisting from the vapour phase is proposed to result from surface energy-driven effects owing to aggregation of fibrous crystals. Theoretical models capable of describing twisting in solution-grown crystals are additionally presented.;Solid-state experiments conducted for EVC and TAEVC revealed the propensity of both compounds to crystallise in isostructural solvated forms. Over a dozen novel solvated salt cocrystals of EVC were isolated and characterised and an initial solid-form landscape was established for TAEVC which had never been subjected to solid-state screening and characterisation previously. The propensity of TAEVC to form a highly robust three-dimensional network of hydrogen bonds in the solid-state was noted, likely an indication of TAEVC molecules' preference to self-associate during nucleation and growth.The research described herein entails solid-state screening investigations and characterisation of a number of active pharmaceutical ingredients to improve understanding of the range of structural diversity possible and specific factors that impact on specific systems. The pharmaceuticals investigated are the anticonvulsant oxcarbazepine (OXCBZ), the developmental cholesterylester transfer protein inhibitor evacetrapib (EVC) and a novel tetrazole analogue of EVC (TAEVC) that has been custom synthesised by Eli Lilly and Company. All three compounds were subjected to both solvent-based and solvent-free crystallisation protocols to target factors promoting the formation of novel crystal structures, characterise any novel crystal packing arrangements formed and explore how non-classical nucleation and growth mechanisms impact crystal morphology.;Experimental screening studies of OXCBZ enabled form III to be obtained via solution crystallisation and from physical vapour deposition onto various substrates. The R-3 crystal structure of this polymorph was determined for the first time using X-ray powder diffraction. OXCBZ form III was also found to crystallise with a striking twisted morphology. Detailed analysis using scanning electron and atomic force microscopy allowed tracking of the formation and evolution of the twisted crystals. Twisting from the vapour phase is proposed to result from surface energy-driven effects owing to aggregation of fibrous crystals. Theoretical models capable of describing twisting in solution-grown crystals are additionally presented.;Solid-state experiments conducted for EVC and TAEVC revealed the propensity of both compounds to crystallise in isostructural solvated forms. Over a dozen novel solvated salt cocrystals of EVC were isolated and characterised and an initial solid-form landscape was established for TAEVC which had never been subjected to solid-state screening and characterisation previously. The propensity of TAEVC to form a highly robust three-dimensional network of hydrogen bonds in the solid-state was noted, likely an indication of TAEVC molecules' preference to self-associate during nucleation and growth
Heterogeneity in the vascular endothelium enables parallel processing of multiple stimuli
Get PDFThe endothelium is a complex network of cells that lines the entire vasculature and it controls virtually all cardiovascular functions. Changes in the behaviour of endothelial cells underly almost all cardiovascular disease. To regulate cardiovascular function, the endothelium integrates hundreds of signals that provide constant instructions. Signals arrive from as close as neighbouring endothelial cells and underlying smooth muscle cells to substances circulating from the most remote outpost of the body. These signals provide endless streams of information that must be integrated and decoded. How this is achieved is not understood. Therefore, the aim of this thesis is to investigate the mechanisms involved in endothelial Ca2+ signalling to muscarinic, purinergic and histaminergic activators and how the endothelium manages these extracellular signals when multiple agonists are present.;Using en face artery preparations we recorded the concurrent Ca2+ activity from hundreds of endothelial cells in intact resistance arteries. The results show that the endothelium is not a homogenous population of cells. Instead, spatially-distinct endothelial cells are primed to detect specific extracellular signals. These spatially distinct cells are arranged in clusters and there is minimal overlap in agonist sensitivity between various clusters. By organising distinct subpopulations of cells to detect specific extracellular signals, the endothelium is able to carry out multiple, completely separate, functions in parallel. In response to each type of extracellular signal, cells generate intracellular messages that have unique characteristics.;When multiple extracellular signals are present together, messages are communicated across cells and computations carried out to generate new signals that are a composite of the inputs. These results suggest individual endothelial cells communicate with their neighbours and complex computations are carried out by combining the information from each source to generate a distinct output. These emergent properties of the endothelium generatea system in which the whole is not equal to the sum of the individual parts. This thesis also describes an inexpensive and flexible pressure myograph system, VasoTracker, which permits the vascular activity of isolated, pressurized bloodvessels to be monitored. The system includes all components that would be expected from a commercial pressure myograph system. VasoTracker is an open source system that makes use of existing hardware and software open source solutions.The endothelium is a complex network of cells that lines the entire vasculature and it controls virtually all cardiovascular functions. Changes in the behaviour of endothelial cells underly almost all cardiovascular disease. To regulate cardiovascular function, the endothelium integrates hundreds of signals that provide constant instructions. Signals arrive from as close as neighbouring endothelial cells and underlying smooth muscle cells to substances circulating from the most remote outpost of the body. These signals provide endless streams of information that must be integrated and decoded. How this is achieved is not understood. Therefore, the aim of this thesis is to investigate the mechanisms involved in endothelial Ca2+ signalling to muscarinic, purinergic and histaminergic activators and how the endothelium manages these extracellular signals when multiple agonists are present.;Using en face artery preparations we recorded the concurrent Ca2+ activity from hundreds of endothelial cells in intact resistance arteries. The results show that the endothelium is not a homogenous population of cells. Instead, spatially-distinct endothelial cells are primed to detect specific extracellular signals. These spatially distinct cells are arranged in clusters and there is minimal overlap in agonist sensitivity between various clusters. By organising distinct subpopulations of cells to detect specific extracellular signals, the endothelium is able to carry out multiple, completely separate, functions in parallel. In response to each type of extracellular signal, cells generate intracellular messages that have unique characteristics.;When multiple extracellular signals are present together, messages are communicated across cells and computations carried out to generate new signals that are a composite of the inputs. These results suggest individual endothelial cells communicate with their neighbours and complex computations are carried out by combining the information from each source to generate a distinct output. These emergent properties of the endothelium generatea system in which the whole is not equal to the sum of the individual parts. This thesis also describes an inexpensive and flexible pressure myograph system, VasoTracker, which permits the vascular activity of isolated, pressurized bloodvessels to be monitored. The system includes all components that would be expected from a commercial pressure myograph system. VasoTracker is an open source system that makes use of existing hardware and software open source solutions
Investigating state-dependent pontine cholinergic activity during REM sleep and P-waves in vivo
Get PDFRapid eye movement (REM) sleep is a behavioural state during which phasic deflections in pontine LFP, known as pontine waves (P-waves), occur most prominently. Cholinergic pedunculopontine and laterodorsal tegmental nucleus (PPT/LDT) neurons have been implicated in the generation mechanisms of both REM sleep and P-waves. However, given that the PPT/LDT also contains glutamatergic and GABAergic neurons, and is surrounded by other sleep-wake regulating structures, the functional role of cholinergic PPT/LDT neurons has been difficult to discern with traditional electrophysiological, lesion and pharmacological studies.;Consequently, contradictory results have been reported and a clear understanding of cell-type specific neural dynamics during REM sleep and P-waves is still lacking. Based on previous studies, it was hypothesised that cholinergic PPT/LDT neuronal activity is correlated with REM sleep initiation and P-waves. To investigate this, a fibre photometry system was designed and built to simultaneously monitor GCaMP6s fluorescence activity from cholinergic PPT/LDT neurons, EEG/EMG signals for polysomnography and pontine LFP for P-wave detection during the sleep-wake cycle in freely moving mice.;Results show that cholinergic PPT/LDT activity is greatest during REM sleep, with underlying rhythmic fluctuations. P-waves were more frequent during REM sleep and for the first time, this study shows that P-waves during REM sleep coincide with larger increases in calcium transients compared to NREM sleep in mice. Additionally, peaks in GCaMP6s fluorescence peaks co-occur with P-wave during REM sleep. As sleep disturbances and cholinergic degeneration are associated with Alzheimer's Disease (AD), a pilot study was carried out to investigate the effects of AD pathology on sleep, PPT/LDT cholinergic activity and P-waves.;Preliminary results show that sleep disturbances were more profound in aging 5XFAD mice, a mouse model of AD, compared to controls. It is concluded that cholinergic PPT/LDT activity coincides with REM sleep initiation and P-waves, however functional studies are required to determine the causal relationship for both phenomena.Rapid eye movement (REM) sleep is a behavioural state during which phasic deflections in pontine LFP, known as pontine waves (P-waves), occur most prominently. Cholinergic pedunculopontine and laterodorsal tegmental nucleus (PPT/LDT) neurons have been implicated in the generation mechanisms of both REM sleep and P-waves. However, given that the PPT/LDT also contains glutamatergic and GABAergic neurons, and is surrounded by other sleep-wake regulating structures, the functional role of cholinergic PPT/LDT neurons has been difficult to discern with traditional electrophysiological, lesion and pharmacological studies.;Consequently, contradictory results have been reported and a clear understanding of cell-type specific neural dynamics during REM sleep and P-waves is still lacking. Based on previous studies, it was hypothesised that cholinergic PPT/LDT neuronal activity is correlated with REM sleep initiation and P-waves. To investigate this, a fibre photometry system was designed and built to simultaneously monitor GCaMP6s fluorescence activity from cholinergic PPT/LDT neurons, EEG/EMG signals for polysomnography and pontine LFP for P-wave detection during the sleep-wake cycle in freely moving mice.;Results show that cholinergic PPT/LDT activity is greatest during REM sleep, with underlying rhythmic fluctuations. P-waves were more frequent during REM sleep and for the first time, this study shows that P-waves during REM sleep coincide with larger increases in calcium transients compared to NREM sleep in mice. Additionally, peaks in GCaMP6s fluorescence peaks co-occur with P-wave during REM sleep. As sleep disturbances and cholinergic degeneration are associated with Alzheimer's Disease (AD), a pilot study was carried out to investigate the effects of AD pathology on sleep, PPT/LDT cholinergic activity and P-waves.;Preliminary results show that sleep disturbances were more profound in aging 5XFAD mice, a mouse model of AD, compared to controls. It is concluded that cholinergic PPT/LDT activity coincides with REM sleep initiation and P-waves, however functional studies are required to determine the causal relationship for both phenomena
Classification of Arabic extremist web content through Arabic textual analysis
Get PDFMany scholars have attempted to study the written and spoken word of terrorist groups and individuals to understand the underlying motivation for terrorist acts. However, until today, those scholars have not made use of automated linguistic analysis programs, especially those focusing on Arabic corpus, in an attempt to understand the mentality behind terrorist acts. A contribution in this regard will be made.The division and classification of texts is an important science of linguistics, whether Arabic or otherwise, it summarizes the effort and time consuming of humans to classify these language texts. The importance of this research stems from not only the importance of the classification itself, but also a classification based on the extreme orientation of these linguistic texts.In this research, the researcher has tried to prove his new methodology based on the division and classification of Arabic texts, a classification that distinguishes them from others according to the identity of the speakers, whether as extremists or against extremism or as neutral people who do not have any ideas belonging to any terrorist or counter category. This methodology is a numerical methodology that relies on dividing speech using two different tools and then analyzing the results using more than six algorithms in the Wiccan program. The researcher got very good results that make the judgment on this methodology a resounding success.This thesis aims to put forward a comprehensive and detailed classification system to categorize different Arabic-speaking website pages with unscrupulous intentions and questionable language. It uses three specific Arabic corpora, (Pro-terrorism, Anti-terrorism, and neutral), from more than 7000 Arabic text to construct corpus (1,000,000 words approx.) from different sites and sources.The division and classification of texts is an important science of linguistics, whether Arabic or otherwise, it summarizes the effort and time consuming of humans to classify these language texts. The importance of this research stems not only from the importance of the classification itself, but also a classification based on the extreme orientation of these linguistic texts.;In this research, the researcher has tried to prove his new methodology based on the division and classification of Arabic texts, a classification that distinguishes them from others according to the identity of the speakers, whether as extremists or against extremism or as neutral people who do not have any ideas belonging to any terrorist or counter category. This methodology is a numerical methodology that relies on dividing speech using two different tools and then analyzing the results using more than six algorithms in the WEKA program. The researcher got very good results that make the judgment on this methodology success.This thesis employs a quantitative approach by using different algorithms (supervised) to build a model for data classification by using manually categorized information. The classification algorithm used to construct the model uses quantitative information extracted by Posit or SAFAR textual analysis framework. This model functions with (58) features combined from Posit - n-grams and morphological SAFAR V2 POS tools. This model achieved more than (94 %) success in the level of precision.This model uses Posit method to make appropriate changes to the code so it can deal with Arabic content, secondly SAFAR V2, which is more suited to the domain of Arabic being based on analyzing the morphology of the word, and therefore, it can highlight all the essential features overlooked in Posit. This model has manual classification, pre-processing steps and can apply eight different experiments using WEKA APIs, a GUI (Graphical user interface) application.The research concludes that the best results reaching 94% precision have been achieved by combining Posit + SAFAR + (18 attributes Posit+ SAFAR N-Gram). Moreover, the most reliable results have been achieved by applying a Random Forest classification algorithm using regression. The research recommends working more on this topic and using new algorithms and techniques.Many scholars have attempted to study the written and spoken word of terrorist groups and individuals to understand the underlying motivation for terrorist acts. However, until today, those scholars have not made use of automated linguistic analysis programs, especially those focusing on Arabic corpus, in an attempt to understand the mentality behind terrorist acts. A contribution in this regard will be made.The division and classification of texts is an important science of linguistics, whether Arabic or otherwise, it summarizes the effort and time consuming of humans to classify these language texts. The importance of this research stems from not only the importance of the classification itself, but also a classification based on the extreme orientation of these linguistic texts.In this research, the researcher has tried to prove his new methodology based on the division and classification of Arabic texts, a classification that distinguishes them from others according to the identity of the speakers, whether as extremists or against extremism or as neutral people who do not have any ideas belonging to any terrorist or counter category. This methodology is a numerical methodology that relies on dividing speech using two different tools and then analyzing the results using more than six algorithms in the Wiccan program. The researcher got very good results that make the judgment on this methodology a resounding success.This thesis aims to put forward a comprehensive and detailed classification system to categorize different Arabic-speaking website pages with unscrupulous intentions and questionable language. It uses three specific Arabic corpora, (Pro-terrorism, Anti-terrorism, and neutral), from more than 7000 Arabic text to construct corpus (1,000,000 words approx.) from different sites and sources.The division and classification of texts is an important science of linguistics, whether Arabic or otherwise, it summarizes the effort and time consuming of humans to classify these language texts. The importance of this research stems not only from the importance of the classification itself, but also a classification based on the extreme orientation of these linguistic texts.;In this research, the researcher has tried to prove his new methodology based on the division and classification of Arabic texts, a classification that distinguishes them from others according to the identity of the speakers, whether as extremists or against extremism or as neutral people who do not have any ideas belonging to any terrorist or counter category. This methodology is a numerical methodology that relies on dividing speech using two different tools and then analyzing the results using more than six algorithms in the WEKA program. The researcher got very good results that make the judgment on this methodology success.This thesis employs a quantitative approach by using different algorithms (supervised) to build a model for data classification by using manually categorized information. The classification algorithm used to construct the model uses quantitative information extracted by Posit or SAFAR textual analysis framework. This model functions with (58) features combined from Posit - n-grams and morphological SAFAR V2 POS tools. This model achieved more than (94 %) success in the level of precision.This model uses Posit method to make appropriate changes to the code so it can deal with Arabic content, secondly SAFAR V2, which is more suited to the domain of Arabic being based on analyzing the morphology of the word, and therefore, it can highlight all the essential features overlooked in Posit. This model has manual classification, pre-processing steps and can apply eight different experiments using WEKA APIs, a GUI (Graphical user interface) application.The research concludes that the best results reaching 94% precision have been achieved by combining Posit + SAFAR + (18 attributes Posit+ SAFAR N-Gram). Moreover, the most reliable results have been achieved by applying a Random Forest classification algorithm using regression. The research recommends working more on this topic and using new algorithms and techniques
Location fingerprinting for IoT systems using machine learning
Get PDFThe Internet of Things (IoT) has evolved rapidly as the number of connected nodes continues to grow, projected to be in excess of Trillions worldwide by 2025. IoT enables a number of application and services that enhance the quality of life of citizens and business practice. The demand for IoT-like connectivity is set to continue; for example, the advent of LPWAN providing a combination of advantageous features such as long-range, low power connectivity gates the deployments of a range of hitherto costly implementations over extended areas of coverage.;A spectrum of valuable real-world IoT applications such as tracking, are predicated on location information. However, the provision of a low power, cost effective engineered solution to provisioning location still remains a major challenge, especially within resource constrained IoT deployments. GPS-enabled solutions are power hungry and potentially prohibitively expensive within extensive IoT architectures. Furthermore, ranging-based network-centric methods lack accuracy because of the long distances subject to dynamically varying path characteristics and the ultra-narrow bandwidth. The prevailing state-of-the-art motivates investigations into low-complexity, energy-efficient technique for IoT node localisation.;The Thesis presents an empirical investigation into the use of fingerprinting for IoT node localisation within a suburban region in Saudi Arabia subject to varying environmental conditions, ranging from clear sky to sandstorms. The approach is based on the use of Received Signal Strength Indicator (RSSI) within a LoRaWAN network setting.;The performance of LoRa transmission as a function of varying coding parameters is determined. The RSSI data gathered during the characterisation phase is exploited to estimate locations of IoT nodes using location fingerprinting. More specifically, k-Nearest Neighbour (KNN) algorithms are used to develop a baseline location model.;The accuracy of the LoRaWAN based baseline node localisation is enhanced through the use of Machine Learning (ML). RSSI ratios between pairs of Gateways in conjunction with kernel-based ML techniques - Support Vector Regression (SVR) and Gaussian Process Regression (GPR) - is proven to improve the node localisation models. Moreover, the impact of the kernel function on model performance is evaluated. Further, RSSI measurements at different spreading factors are combined to form more robust location features; two machine learning ensemble techniques - Gradient Boosting and Random Forest - are then employed to determine the impact on the accuracy of node localisation models using combined location features. Results indicate that ensemble-derived models improve accuracy compared to single regression tree methods. In addition, feature transformation is proven to be effective in improving localisation performance.;Results confirm the feasibility of IoT network-derived localisation in sandstorm environments. Furthermore, it is demonstrated that the LoRaWAN spreading factor is central to optimising performance.The Internet of Things (IoT) has evolved rapidly as the number of connected nodes continues to grow, projected to be in excess of Trillions worldwide by 2025. IoT enables a number of application and services that enhance the quality of life of citizens and business practice. The demand for IoT-like connectivity is set to continue; for example, the advent of LPWAN providing a combination of advantageous features such as long-range, low power connectivity gates the deployments of a range of hitherto costly implementations over extended areas of coverage.;A spectrum of valuable real-world IoT applications such as tracking, are predicated on location information. However, the provision of a low power, cost effective engineered solution to provisioning location still remains a major challenge, especially within resource constrained IoT deployments. GPS-enabled solutions are power hungry and potentially prohibitively expensive within extensive IoT architectures. Furthermore, ranging-based network-centric methods lack accuracy because of the long distances subject to dynamically varying path characteristics and the ultra-narrow bandwidth. The prevailing state-of-the-art motivates investigations into low-complexity, energy-efficient technique for IoT node localisation.;The Thesis presents an empirical investigation into the use of fingerprinting for IoT node localisation within a suburban region in Saudi Arabia subject to varying environmental conditions, ranging from clear sky to sandstorms. The approach is based on the use of Received Signal Strength Indicator (RSSI) within a LoRaWAN network setting.;The performance of LoRa transmission as a function of varying coding parameters is determined. The RSSI data gathered during the characterisation phase is exploited to estimate locations of IoT nodes using location fingerprinting. More specifically, k-Nearest Neighbour (KNN) algorithms are used to develop a baseline location model.;The accuracy of the LoRaWAN based baseline node localisation is enhanced through the use of Machine Learning (ML). RSSI ratios between pairs of Gateways in conjunction with kernel-based ML techniques - Support Vector Regression (SVR) and Gaussian Process Regression (GPR) - is proven to improve the node localisation models. Moreover, the impact of the kernel function on model performance is evaluated. Further, RSSI measurements at different spreading factors are combined to form more robust location features; two machine learning ensemble techniques - Gradient Boosting and Random Forest - are then employed to determine the impact on the accuracy of node localisation models using combined location features. Results indicate that ensemble-derived models improve accuracy compared to single regression tree methods. In addition, feature transformation is proven to be effective in improving localisation performance.;Results confirm the feasibility of IoT network-derived localisation in sandstorm environments. Furthermore, it is demonstrated that the LoRaWAN spreading factor is central to optimising performance
An experimental method to measure crystal growth rate in porous materials and quantity deformation developed during crystal growth
Get PDFThe aim of this thesis work is to develop a new experimental method to quantify dam-ages produced by salt crystallisation in porous building materials, a phenomenon long considered as one of the most important cause of deterioration problems, occurring in monuments exposed to a wide range of environmental conditions [125]. Sodium sulphate in particular is widely recognised to be the most damaging salt because of the difference in solubility between phases and the very temperature-sensitive solubility of the stable phase at room temperature. It is known to have two hydrated phases at ambient conditions: the metastable heptahydrate (Na2SO4 .7H2O) and the stable decahydrate called mirabilite (Na2SO4 . 10H2O). Damage is caused by mirabilite crystallisation from supersaturated solutions, either directly or via heptahydrate dissolution, which develops a high crystallisation pressure and relative high strain level on the porous matrix. Crystallisation of sodium sulphate hydrates is followed by ice formation when temperature drops below the solution eutectic point (c.-3°C). These mechanisms result in micro and macro fractures of the building materials. Damages entail costs from repair/re-placement, hence a deeper quantitative understanding of strain produced from salt and ice crystallization in building materials is extremely relevant. With the new methodo-logy presented here it can be measured how fast sodium sulphate and ice crystals grow through building stones and what strain is developed as a result. We achieved this by developing a new experimental apparatus which measures sample strain and crystal propagation rate using an LVDT and thermocouples respectively. For this thesis work we also used X-ray computed tomography (X-CT) to investigate where salts tend to distribute within stone cores. This technique enables one to image the morphology and internal structure of porous material, at very high resolution (µm), hence salt distribution within the porous matrix and at its outer surface could be detected. Understanding where different salts tend to distribute is fundamental to estimate and predict damage they can cause. Lastly, we also explored how specific stone physical and chemical parameters can affect damage caused by salts to masonry, and how damage is related to environmental parameters such as relative humidity and temperature and their cyclical changes. We did this by carrying out a field investigation at the Shetland Island Town Hall where sodium sulphate was found. For the investigation we used the apparatus developed during this work plus other diagnostic techniques.The aim of this thesis work is to develop a new experimental method to quantify dam-ages produced by salt crystallisation in porous building materials, a phenomenon long considered as one of the most important cause of deterioration problems, occurring in monuments exposed to a wide range of environmental conditions [125]. Sodium sulphate in particular is widely recognised to be the most damaging salt because of the difference in solubility between phases and the very temperature-sensitive solubility of the stable phase at room temperature. It is known to have two hydrated phases at ambient conditions: the metastable heptahydrate (Na2SO4 .7H2O) and the stable decahydrate called mirabilite (Na2SO4 . 10H2O). Damage is caused by mirabilite crystallisation from supersaturated solutions, either directly or via heptahydrate dissolution, which develops a high crystallisation pressure and relative high strain level on the porous matrix. Crystallisation of sodium sulphate hydrates is followed by ice formation when temperature drops below the solution eutectic point (c.-3°C). These mechanisms result in micro and macro fractures of the building materials. Damages entail costs from repair/re-placement, hence a deeper quantitative understanding of strain produced from salt and ice crystallization in building materials is extremely relevant. With the new methodo-logy presented here it can be measured how fast sodium sulphate and ice crystals grow through building stones and what strain is developed as a result. We achieved this by developing a new experimental apparatus which measures sample strain and crystal propagation rate using an LVDT and thermocouples respectively. For this thesis work we also used X-ray computed tomography (X-CT) to investigate where salts tend to distribute within stone cores. This technique enables one to image the morphology and internal structure of porous material, at very high resolution (µm), hence salt distribution within the porous matrix and at its outer surface could be detected. Understanding where different salts tend to distribute is fundamental to estimate and predict damage they can cause. Lastly, we also explored how specific stone physical and chemical parameters can affect damage caused by salts to masonry, and how damage is related to environmental parameters such as relative humidity and temperature and their cyclical changes. We did this by carrying out a field investigation at the Shetland Island Town Hall where sodium sulphate was found. For the investigation we used the apparatus developed during this work plus other diagnostic techniques
On the role of entanglement in the out-of-equilibrium dynamics of many-body quantum systems
Get PDFUnderstanding the behaviour of many-body quantum systems is one of the great challenges in physics. Both at and out of equilibrium, besides few exactly solvable cases, our understanding relies on numerical simulations. Unfortunately, simulating many-body quantum systems is a hard-computational problem. The standard lore is that this problem is exponentially hard in the case of simulating many-body quantum systems out-of-equilibrium. Even for simple systems, such as 1D spin chains, the current algorithms, based on the time-dependent density matrix renormalization group, are exponentially expensive in the amount of entanglement in the system. In generic out-of-equilibrium scenarios, the amount of entanglement grows linearly with time, resulting in exponentially expensive simulations. In the last years, however, the developments of the experimental techniques for controlling many-body quantum systems have pushed the exploration of out-of-equilibrium many-body quantum systems further. A critical assessment of the scope and limitations of classical numerical simulations that could help to both validate and understand the new experiments is thus necessary.;In this thesis we address this issue by unveiling the real role entanglement has in limiting our ability to simulate many-body quantum systems out-of-equilibrium. In particular, we first develop the tools for numerical computations. We build a comprehensive library for the manipulation of Fermionic Gaussian States with the programming language Julia. Then, we proceed to design and characterize a specific algorithm that allows to systematically approximate the equilibration value of local operators after a quantum quench. At the core of this algorithm there is the idea of transforming entanglement between distant parts of the system into mixture, while at the same time preserving the local reduced density matrices. Finally, we show that, for the Ising model, during the out-of-equilibrium evolution the entanglement spectrum allows us to obtain universal information. This information encodes the data of the underlying conformal field theory describing the system at the critical point, suggesting that it should be possible to adopt an analytical approach based on conformal field theories to obtain information about the out-of-equilibrium dynamics.Understanding the behaviour of many-body quantum systems is one of the great challenges in physics. Both at and out of equilibrium, besides few exactly solvable cases, our understanding relies on numerical simulations. Unfortunately, simulating many-body quantum systems is a hard-computational problem. The standard lore is that this problem is exponentially hard in the case of simulating many-body quantum systems out-of-equilibrium. Even for simple systems, such as 1D spin chains, the current algorithms, based on the time-dependent density matrix renormalization group, are exponentially expensive in the amount of entanglement in the system. In generic out-of-equilibrium scenarios, the amount of entanglement grows linearly with time, resulting in exponentially expensive simulations. In the last years, however, the developments of the experimental techniques for controlling many-body quantum systems have pushed the exploration of out-of-equilibrium many-body quantum systems further. A critical assessment of the scope and limitations of classical numerical simulations that could help to both validate and understand the new experiments is thus necessary.;In this thesis we address this issue by unveiling the real role entanglement has in limiting our ability to simulate many-body quantum systems out-of-equilibrium. In particular, we first develop the tools for numerical computations. We build a comprehensive library for the manipulation of Fermionic Gaussian States with the programming language Julia. Then, we proceed to design and characterize a specific algorithm that allows to systematically approximate the equilibration value of local operators after a quantum quench. At the core of this algorithm there is the idea of transforming entanglement between distant parts of the system into mixture, while at the same time preserving the local reduced density matrices. Finally, we show that, for the Ising model, during the out-of-equilibrium evolution the entanglement spectrum allows us to obtain universal information. This information encodes the data of the underlying conformal field theory describing the system at the critical point, suggesting that it should be possible to adopt an analytical approach based on conformal field theories to obtain information about the out-of-equilibrium dynamics
The impact of introducing zonal pricing within GB on investment signals to low carbon generation
Get PDFThis work has investigated the premise that utilising zonal pricing for congestion management within Great Britain (GB) with Scotland as a separate price zone than the rest of GB could disincentivise investment in wind generation within areas of the highest wind resource. Computational modelling has shown consistently less installed wind capacity in Scotland in scenarios representing zonal pricing compared with scenarios representing the current GB system. This suggests that in the long term implementing zonal pricing within GB could negatively impact on the investment of low carbon generation in locations with the best renewable resource, which would be the most cost-effective method of meeting carbon reduction targets under the UK Levy Control Framework;The interaction between investing in low carbon generation within multiple price zones and the subsidy framework including a feed-in tariff with Contracts for Difference (CfDs) is a key focus of this work. Multiple scenarios are developed following a discussion of form that the CfD scheme could take in a two-zone GB. These comprise of a base case scenario representing current electricity trading within GB, a scenario in which the current competitive auction system does not change and CfD strike prices remain GB-wide and a scenario in which locational strike prices are introduced.;Computational modelling has taken the form of a two-node linear solver to introduce and discuss the potential impacts of two price zones in GB on investment in low carbon generation and the Scottish Electricity Dispatch Model (SEDM), an eighteen node investment and dispatch model with greater spatial and temporal complexity and thus a more accurate representation of the GB system. The modelling methodology includes representing a range of objective functions, which has been shown to significantly affect the zonal results. Cases have also been revealed in which the SRMC iteration process did not converge for the two zone solver, highlighting the potential issues involved with modelling a subsidy framework like the CfD mechanism within multiple price zones.This work has investigated the premise that utilising zonal pricing for congestion management within Great Britain (GB) with Scotland as a separate price zone than the rest of GB could disincentivise investment in wind generation within areas of the highest wind resource. Computational modelling has shown consistently less installed wind capacity in Scotland in scenarios representing zonal pricing compared with scenarios representing the current GB system. This suggests that in the long term implementing zonal pricing within GB could negatively impact on the investment of low carbon generation in locations with the best renewable resource, which would be the most cost-effective method of meeting carbon reduction targets under the UK Levy Control Framework;The interaction between investing in low carbon generation within multiple price zones and the subsidy framework including a feed-in tariff with Contracts for Difference (CfDs) is a key focus of this work. Multiple scenarios are developed following a discussion of form that the CfD scheme could take in a two-zone GB. These comprise of a base case scenario representing current electricity trading within GB, a scenario in which the current competitive auction system does not change and CfD strike prices remain GB-wide and a scenario in which locational strike prices are introduced.;Computational modelling has taken the form of a two-node linear solver to introduce and discuss the potential impacts of two price zones in GB on investment in low carbon generation and the Scottish Electricity Dispatch Model (SEDM), an eighteen node investment and dispatch model with greater spatial and temporal complexity and thus a more accurate representation of the GB system. The modelling methodology includes representing a range of objective functions, which has been shown to significantly affect the zonal results. Cases have also been revealed in which the SRMC iteration process did not converge for the two zone solver, highlighting the potential issues involved with modelling a subsidy framework like the CfD mechanism within multiple price zones
Nonlinear photonics with applications in lightwave communications
Get PDFThis doctoral dissertation investigates the use of nonlinear photonics in targeted Lightwave communication applications. Different highly nonlinear optical materials have been considered for the investigation of Lightwave communications data carriers, with a focus on the optical carrier pulsewidth. A state-of-the-art novel method has been developed to measure pico-second optical carrier pulses using highly nonlinear optical fiber. This method is based on the nonlinear optical loop mirror (NOLM), with consideration focused on the third order nonlinearity. Silicon is considered to be one of the most attractive materials for photonics integrated circuit technology (PIC) due to its compatibility with complementary metal oxide semiconductor (CMOS). As such, the method has been applied to the SOI platform Mach-Zehnder interferometer (MZI), also by considering the third order nonlinearity. In the NOLM approach, the picosecond optical data carrier pulsewidth is measured by using an optical power meter. Simulations for both the self-phase and cross-phase modulation schemes are carried out, and as expected, the cross phase modulation gives an increment in the sensitivity twice that of the self-phase modulation. Due to very high repetition rates of the order 10 GHz, the effect of counter propagating non-linear interactions in the NOLM are also considered in the theoretical evaluation. In the experimental validation, the pulses from an active fiber mode-locked laser at a repetition rate of 10 GHz were incrementally temporally dispersed using an SMF-28 fiber. The optical data carrier pulses over a range of 2-10 ps were successfully measured with a resolution of 0.25 ps. By extrapolating the theoretical evaluation and by selecting different physical parameters for the setup, the method was found to exhibit an extended range of 0.25 to 40 ps.;The concept described above is then extended to the investigation of nonlinear SOI devices using an MZI, thus miniaturizing the setup. In this investigation, the silicon waveguide has been simulated for self-phase and cross-phase modulation by solving the nonlinear Schrodinger equations using the split step method. Silicon has strong two photon absorption at telecommunication wavelengths, i.e. 1550 nm, and therefore all nonlinear losses (i.e. TPA and free carriers generated through TPA) are included in the split step simulations. The results obtained show that the on-chip nonlinear MZI (based on the SOI platform) can also be used for the measurement of optical data carrier pulse-widths of up to 10 ps. In the last part of this doctoral dissertation, a novel design for a temperature insensitive MZI is presented. Temperature dependence is one of the main challenges in the design of the SOI platform due to the large thermo-optic coefficient of its core material. A change in temperature can cause the device properties to deviate significantly, and can also alter the nonlinear properties of the device. Therefore, a design of an all-passive athermal MZI device based on the SOI platform has been developed and investigated. The MZI's temperature compensation is achieved by optimizing the relative length of the wire and subwavelength grating arms, and by tailoring the thermal response of the subwavelength structure. The simulation results of the athermal MZI design indicated that an overall temperature sensitivity of 7.5 pm/K could be achieved over a 100 nm spectral range near the 1550 nm region.This doctoral dissertation investigates the use of nonlinear photonics in targeted Lightwave communication applications. Different highly nonlinear optical materials have been considered for the investigation of Lightwave communications data carriers, with a focus on the optical carrier pulsewidth. A state-of-the-art novel method has been developed to measure pico-second optical carrier pulses using highly nonlinear optical fiber. This method is based on the nonlinear optical loop mirror (NOLM), with consideration focused on the third order nonlinearity. Silicon is considered to be one of the most attractive materials for photonics integrated circuit technology (PIC) due to its compatibility with complementary metal oxide semiconductor (CMOS). As such, the method has been applied to the SOI platform Mach-Zehnder interferometer (MZI), also by considering the third order nonlinearity. In the NOLM approach, the picosecond optical data carrier pulsewidth is measured by using an optical power meter. Simulations for both the self-phase and cross-phase modulation schemes are carried out, and as expected, the cross phase modulation gives an increment in the sensitivity twice that of the self-phase modulation. Due to very high repetition rates of the order 10 GHz, the effect of counter propagating non-linear interactions in the NOLM are also considered in the theoretical evaluation. In the experimental validation, the pulses from an active fiber mode-locked laser at a repetition rate of 10 GHz were incrementally temporally dispersed using an SMF-28 fiber. The optical data carrier pulses over a range of 2-10 ps were successfully measured with a resolution of 0.25 ps. By extrapolating the theoretical evaluation and by selecting different physical parameters for the setup, the method was found to exhibit an extended range of 0.25 to 40 ps.;The concept described above is then extended to the investigation of nonlinear SOI devices using an MZI, thus miniaturizing the setup. In this investigation, the silicon waveguide has been simulated for self-phase and cross-phase modulation by solving the nonlinear Schrodinger equations using the split step method. Silicon has strong two photon absorption at telecommunication wavelengths, i.e. 1550 nm, and therefore all nonlinear losses (i.e. TPA and free carriers generated through TPA) are included in the split step simulations. The results obtained show that the on-chip nonlinear MZI (based on the SOI platform) can also be used for the measurement of optical data carrier pulse-widths of up to 10 ps. In the last part of this doctoral dissertation, a novel design for a temperature insensitive MZI is presented. Temperature dependence is one of the main challenges in the design of the SOI platform due to the large thermo-optic coefficient of its core material. A change in temperature can cause the device properties to deviate significantly, and can also alter the nonlinear properties of the device. Therefore, a design of an all-passive athermal MZI device based on the SOI platform has been developed and investigated. The MZI's temperature compensation is achieved by optimizing the relative length of the wire and subwavelength grating arms, and by tailoring the thermal response of the subwavelength structure. The simulation results of the athermal MZI design indicated that an overall temperature sensitivity of 7.5 pm/K could be achieved over a 100 nm spectral range near the 1550 nm region
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